Overview

Dataset statistics

Number of variables13
Number of observations5806
Missing cells7876
Missing cells (%)10.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory589.8 KiB
Average record size in memory104.0 B

Variable types

Numeric6
Text5
Categorical2

Alerts

age_certification is highly overall correlated with runtime and 1 other fieldsHigh correlation
df_index is highly overall correlated with release_year and 1 other fieldsHigh correlation
release_year is highly overall correlated with df_index and 1 other fieldsHigh correlation
runtime is highly overall correlated with age_certification and 1 other fieldsHigh correlation
seasons is highly overall correlated with df_index and 2 other fieldsHigh correlation
type is highly overall correlated with age_certification and 2 other fieldsHigh correlation
age_certification has 2610 (45.0%) missing valuesMissing
seasons has 3759 (64.7%) missing valuesMissing
imdb_id has 444 (7.6%) missing valuesMissing
imdb_score has 523 (9.0%) missing valuesMissing
imdb_votes has 539 (9.3%) missing valuesMissing
df_index is uniformly distributedUniform
df_index has unique valuesUnique
id has unique valuesUnique

Reproduction

Analysis started2023-11-27 20:41:58.878777
Analysis finished2023-11-27 20:42:06.659915
Duration7.78 seconds
Software versionydata-profiling vv4.6.2
Download configurationconfig.json

Variables

df_index
Real number (ℝ)

HIGH CORRELATION  UNIFORM  UNIQUE 

Distinct5806
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2902.5
Minimum0
Maximum5805
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:06.781488image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile290.25
Q11451.25
median2902.5
Q34353.75
95-th percentile5514.75
Maximum5805
Range5805
Interquartile range (IQR)2902.5

Descriptive statistics

Standard deviation1676.1922
Coefficient of variation (CV)0.57749945
Kurtosis-1.2
Mean2902.5
Median Absolute Deviation (MAD)1451.5
Skewness0
Sum16851915
Variance2809620.2
MonotonicityStrictly increasing
2023-11-27T22:42:07.002090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1
 
< 0.1%
3877 1
 
< 0.1%
3875 1
 
< 0.1%
3874 1
 
< 0.1%
3873 1
 
< 0.1%
3872 1
 
< 0.1%
3871 1
 
< 0.1%
3870 1
 
< 0.1%
3869 1
 
< 0.1%
3868 1
 
< 0.1%
Other values (5796) 5796
99.8%
ValueCountFrequency (%)
0 1
< 0.1%
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
ValueCountFrequency (%)
5805 1
< 0.1%
5804 1
< 0.1%
5803 1
< 0.1%
5802 1
< 0.1%
5801 1
< 0.1%
5800 1
< 0.1%
5799 1
< 0.1%
5798 1
< 0.1%
5797 1
< 0.1%
5796 1
< 0.1%

id
Text

UNIQUE 

Distinct5806
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:07.390932image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length9
Median length8
Mean length7.8024457
Min length3

Characters and Unicode

Total characters45301
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5806 ?
Unique (%)100.0%

Sample

1st rowts300399
2nd rowtm84618
3rd rowtm127384
4th rowtm70993
5th rowtm190788
ValueCountFrequency (%)
ts300399 1
 
< 0.1%
tm70993 1
 
< 0.1%
ts22164 1
 
< 0.1%
tm14873 1
 
< 0.1%
tm185072 1
 
< 0.1%
tm98978 1
 
< 0.1%
tm119281 1
 
< 0.1%
tm67378 1
 
< 0.1%
tm44204 1
 
< 0.1%
tm69778 1
 
< 0.1%
Other values (5796) 5796
99.8%
2023-11-27T22:42:08.104337image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 5806
12.8%
1 4122
9.1%
2 4098
9.0%
m 3759
8.3%
4 3643
8.0%
3 3641
8.0%
8 3641
8.0%
5 3079
 
6.8%
0 2908
 
6.4%
9 2900
 
6.4%
Other values (3) 7704
17.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33689
74.4%
Lowercase Letter 11612
 
25.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 4122
12.2%
2 4098
12.2%
4 3643
10.8%
3 3641
10.8%
8 3641
10.8%
5 3079
9.1%
0 2908
8.6%
9 2900
8.6%
7 2889
8.6%
6 2768
8.2%
Lowercase Letter
ValueCountFrequency (%)
t 5806
50.0%
m 3759
32.4%
s 2047
 
17.6%

Most occurring scripts

ValueCountFrequency (%)
Common 33689
74.4%
Latin 11612
 
25.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 4122
12.2%
2 4098
12.2%
4 3643
10.8%
3 3641
10.8%
8 3641
10.8%
5 3079
9.1%
0 2908
8.6%
9 2900
8.6%
7 2889
8.6%
6 2768
8.2%
Latin
ValueCountFrequency (%)
t 5806
50.0%
m 3759
32.4%
s 2047
 
17.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45301
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 5806
12.8%
1 4122
9.1%
2 4098
9.0%
m 3759
8.3%
4 3643
8.0%
3 3641
8.0%
8 3641
8.0%
5 3079
 
6.8%
0 2908
 
6.4%
9 2900
 
6.4%
Other values (3) 7704
17.0%

title
Text

Distinct5751
Distinct (%)99.1%
Missing1
Missing (%)< 0.1%
Memory size45.5 KiB
2023-11-27T22:42:08.434494image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length104
Median length61
Mean length17.854608
Min length1

Characters and Unicode

Total characters103646
Distinct characters154
Distinct categories16 ?
Distinct scripts6 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5699 ?
Unique (%)98.2%

Sample

1st rowFive Came Back: The Reference Films
2nd rowTaxi Driver
3rd rowMonty Python and the Holy Grail
4th rowLife of Brian
5th rowThe Exorcist
ValueCountFrequency (%)
the 1511
 
8.3%
of 445
 
2.5%
a 265
 
1.5%
in 201
 
1.1%
179
 
1.0%
and 140
 
0.8%
to 138
 
0.8%
love 125
 
0.7%
my 102
 
0.6%
2 71
 
0.4%
Other values (6735) 14940
82.5%
2023-11-27T22:42:08.966069image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12314
 
11.9%
e 9696
 
9.4%
a 7391
 
7.1%
o 5895
 
5.7%
i 5735
 
5.5%
n 5413
 
5.2%
r 5412
 
5.2%
t 4753
 
4.6%
s 4091
 
3.9%
h 3680
 
3.6%
Other values (144) 39266
37.9%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 72233
69.7%
Uppercase Letter 16364
 
15.8%
Space Separator 12314
 
11.9%
Other Punctuation 1989
 
1.9%
Decimal Number 504
 
0.5%
Dash Punctuation 141
 
0.1%
Other Letter 42
 
< 0.1%
Open Punctuation 15
 
< 0.1%
Close Punctuation 15
 
< 0.1%
Math Symbol 10
 
< 0.1%
Other values (6) 19
 
< 0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
e 9696
13.4%
a 7391
10.2%
o 5895
 
8.2%
i 5735
 
7.9%
n 5413
 
7.5%
r 5412
 
7.5%
t 4753
 
6.6%
s 4091
 
5.7%
h 3680
 
5.1%
l 3511
 
4.9%
Other values (40) 16656
23.1%
Other Letter
ValueCountFrequency (%)
า 6
 
14.3%
ว 3
 
7.1%
น 3
 
7.1%
ร 2
 
4.8%
ล 2
 
4.8%
糖 2
 
4.8%
요 1
 
2.4%
타 1
 
2.4%
스 1
 
2.4%
버 1
 
2.4%
Other values (20) 20
47.6%
Uppercase Letter
ValueCountFrequency (%)
T 1864
 
11.4%
S 1505
 
9.2%
M 1216
 
7.4%
B 1105
 
6.8%
C 1040
 
6.4%
A 995
 
6.1%
L 814
 
5.0%
D 809
 
4.9%
H 720
 
4.4%
P 646
 
3.9%
Other values (19) 5650
34.5%
Other Punctuation
ValueCountFrequency (%)
: 1108
55.7%
' 272
 
13.7%
. 150
 
7.5%
& 128
 
6.4%
, 115
 
5.8%
! 108
 
5.4%
? 49
 
2.5%
* 25
 
1.3%
/ 13
 
0.7%
# 9
 
0.5%
Other values (7) 12
 
0.6%
Decimal Number
ValueCountFrequency (%)
2 130
25.8%
0 81
16.1%
1 76
15.1%
3 49
 
9.7%
4 35
 
6.9%
9 35
 
6.9%
8 26
 
5.2%
6 25
 
5.0%
5 24
 
4.8%
7 23
 
4.6%
Nonspacing Mark
ValueCountFrequency (%)
้ 2
40.0%
ั 1
20.0%
ิ 1
20.0%
่ 1
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 135
95.7%
– 6
 
4.3%
Math Symbol
ValueCountFrequency (%)
+ 8
80.0%
~ 2
 
20.0%
Final Punctuation
ValueCountFrequency (%)
’ 6
85.7%
” 1
 
14.3%
Other Number
ValueCountFrequency (%)
½ 1
50.0%
² 1
50.0%
Space Separator
ValueCountFrequency (%)
12314
100.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 3
100.0%
Currency Symbol
ValueCountFrequency (%)
$ 1
100.0%
Initial Punctuation
ValueCountFrequency (%)
“ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 88590
85.5%
Common 15002
 
14.5%
Thai 30
 
< 0.1%
Hangul 11
 
< 0.1%
Cyrillic 7
 
< 0.1%
Han 6
 
< 0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
e 9696
 
10.9%
a 7391
 
8.3%
o 5895
 
6.7%
i 5735
 
6.5%
n 5413
 
6.1%
r 5412
 
6.1%
t 4753
 
5.4%
s 4091
 
4.6%
h 3680
 
4.2%
l 3511
 
4.0%
Other values (62) 33013
37.3%
Common
ValueCountFrequency (%)
12314
82.1%
: 1108
 
7.4%
' 272
 
1.8%
. 150
 
1.0%
- 135
 
0.9%
2 130
 
0.9%
& 128
 
0.9%
, 115
 
0.8%
! 108
 
0.7%
0 81
 
0.5%
Other values (31) 461
 
3.1%
Thai
ValueCountFrequency (%)
า 6
20.0%
ว 3
 
10.0%
น 3
 
10.0%
ร 2
 
6.7%
ล 2
 
6.7%
้ 2
 
6.7%
ส 1
 
3.3%
เ 1
 
3.3%
ม 1
 
3.3%
ง 1
 
3.3%
Other values (8) 8
26.7%
Hangul
ValueCountFrequency (%)
요 1
9.1%
타 1
9.1%
스 1
9.1%
버 1
9.1%
법 1
9.1%
마 1
9.1%
캐 1
9.1%
치 1
9.1%
티 1
9.1%
니 1
9.1%
Cyrillic
ValueCountFrequency (%)
о 1
14.3%
т 1
14.3%
Т 1
14.3%
р 1
14.3%
и 1
14.3%
к 1
14.3%
а 1
14.3%
Han
ValueCountFrequency (%)
糖 2
33.3%
料 1
16.7%
幸 1
16.7%
福 1
16.7%
理 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103465
99.8%
None 111
 
0.1%
Thai 30
 
< 0.1%
Punctuation 16
 
< 0.1%
Hangul 11
 
< 0.1%
Cyrillic 7
 
< 0.1%
CJK 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12314
 
11.9%
e 9696
 
9.4%
a 7391
 
7.1%
o 5895
 
5.7%
i 5735
 
5.5%
n 5413
 
5.2%
r 5412
 
5.2%
t 4753
 
4.6%
s 4091
 
4.0%
h 3680
 
3.6%
Other values (73) 39085
37.8%
None
ValueCountFrequency (%)
é 24
21.6%
í 20
18.0%
á 15
13.5%
ñ 11
9.9%
ó 10
9.0%
ü 4
 
3.6%
ú 3
 
2.7%
ı 3
 
2.7%
ô 3
 
2.7%
· 2
 
1.8%
Other values (15) 16
14.4%
Punctuation
ValueCountFrequency (%)
’ 6
37.5%
– 6
37.5%
… 2
 
12.5%
“ 1
 
6.2%
” 1
 
6.2%
Thai
ValueCountFrequency (%)
า 6
20.0%
ว 3
 
10.0%
น 3
 
10.0%
ร 2
 
6.7%
ล 2
 
6.7%
้ 2
 
6.7%
ส 1
 
3.3%
เ 1
 
3.3%
ม 1
 
3.3%
ง 1
 
3.3%
Other values (8) 8
26.7%
CJK
ValueCountFrequency (%)
糖 2
33.3%
料 1
16.7%
幸 1
16.7%
福 1
16.7%
理 1
16.7%
Cyrillic
ValueCountFrequency (%)
о 1
14.3%
т 1
14.3%
Т 1
14.3%
р 1
14.3%
и 1
14.3%
к 1
14.3%
а 1
14.3%
Hangul
ValueCountFrequency (%)
요 1
9.1%
타 1
9.1%
스 1
9.1%
버 1
9.1%
법 1
9.1%
마 1
9.1%
캐 1
9.1%
치 1
9.1%
티 1
9.1%
니 1
9.1%

type
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size45.5 KiB
MOVIE
3759 
SHOW
2047 

Length

Max length5
Median length5
Mean length4.6474337
Min length4

Characters and Unicode

Total characters26983
Distinct characters8
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowSHOW
2nd rowMOVIE
3rd rowMOVIE
4th rowMOVIE
5th rowMOVIE

Common Values

ValueCountFrequency (%)
MOVIE 3759
64.7%
SHOW 2047
35.3%

Length

2023-11-27T22:42:09.187440image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-11-27T22:42:09.426333image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
ValueCountFrequency (%)
movie 3759
64.7%
show 2047
35.3%

Most occurring characters

ValueCountFrequency (%)
O 5806
21.5%
M 3759
13.9%
V 3759
13.9%
I 3759
13.9%
E 3759
13.9%
S 2047
 
7.6%
H 2047
 
7.6%
W 2047
 
7.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 26983
100.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 5806
21.5%
M 3759
13.9%
V 3759
13.9%
I 3759
13.9%
E 3759
13.9%
S 2047
 
7.6%
H 2047
 
7.6%
W 2047
 
7.6%

Most occurring scripts

ValueCountFrequency (%)
Latin 26983
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 5806
21.5%
M 3759
13.9%
V 3759
13.9%
I 3759
13.9%
E 3759
13.9%
S 2047
 
7.6%
H 2047
 
7.6%
W 2047
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 26983
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
O 5806
21.5%
M 3759
13.9%
V 3759
13.9%
I 3759
13.9%
E 3759
13.9%
S 2047
 
7.6%
H 2047
 
7.6%
W 2047
 
7.6%

release_year
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016.0134
Minimum1945
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:09.603117image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1945
5-th percentile2003
Q12015
median2018
Q32020
95-th percentile2021
Maximum2022
Range77
Interquartile range (IQR)5

Descriptive statistics

Standard deviation7.324883
Coefficient of variation (CV)0.0036333503
Kurtosis17.057172
Mean2016.0134
Median Absolute Deviation (MAD)2
Skewness-3.5203185
Sum11704974
Variance53.653911
MonotonicityNot monotonic
2023-11-27T22:42:09.915955image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2019 848
14.6%
2020 805
13.9%
2018 774
13.3%
2021 758
13.1%
2017 580
10.0%
2016 371
6.4%
2015 236
 
4.1%
2022 217
 
3.7%
2014 160
 
2.8%
2013 142
 
2.4%
Other values (57) 915
15.8%
ValueCountFrequency (%)
1945 1
< 0.1%
1953 1
< 0.1%
1954 2
< 0.1%
1956 1
< 0.1%
1958 1
< 0.1%
1959 1
< 0.1%
1960 1
< 0.1%
1961 1
< 0.1%
1962 1
< 0.1%
1963 1
< 0.1%
ValueCountFrequency (%)
2022 217
 
3.7%
2021 758
13.1%
2020 805
13.9%
2019 848
14.6%
2018 774
13.3%
2017 580
10.0%
2016 371
6.4%
2015 236
 
4.1%
2014 160
 
2.8%
2013 142
 
2.4%

age_certification
Categorical

HIGH CORRELATION  MISSING 

Distinct11
Distinct (%)0.3%
Missing2610
Missing (%)45.0%
Memory size45.5 KiB
TV-MA
841 
R
575 
TV-14
470 
PG-13
440 
PG
246 
Other values (6)
624 

Length

Max length5
Median length5
Mean length3.8288486
Min length1

Characters and Unicode

Total characters12237
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowTV-MA
2nd rowR
3rd rowPG
4th rowR
5th rowR

Common Values

ValueCountFrequency (%)
TV-MA 841
 
14.5%
R 575
 
9.9%
TV-14 470
 
8.1%
PG-13 440
 
7.6%
PG 246
 
4.2%
TV-PG 186
 
3.2%
G 131
 
2.3%
TV-Y7 112
 
1.9%
TV-Y 105
 
1.8%
TV-G 76
 
1.3%
(Missing) 2610
45.0%

Length

2023-11-27T22:42:10.164688image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
tv-ma 841
26.3%
r 575
18.0%
tv-14 470
14.7%
pg-13 440
13.8%
pg 246
 
7.7%
tv-pg 186
 
5.8%
g 131
 
4.1%
tv-y7 112
 
3.5%
tv-y 105
 
3.3%
tv-g 76
 
2.4%

Most occurring characters

ValueCountFrequency (%)
- 2244
18.3%
T 1790
14.6%
V 1790
14.6%
G 1079
8.8%
1 924
7.6%
P 872
 
7.1%
M 841
 
6.9%
A 841
 
6.9%
R 575
 
4.7%
4 470
 
3.8%
Other values (5) 811
 
6.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 8033
65.6%
Dash Punctuation 2244
 
18.3%
Decimal Number 1960
 
16.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 1790
22.3%
V 1790
22.3%
G 1079
13.4%
P 872
10.9%
M 841
10.5%
A 841
10.5%
R 575
 
7.2%
Y 217
 
2.7%
N 14
 
0.2%
C 14
 
0.2%
Decimal Number
ValueCountFrequency (%)
1 924
47.1%
4 470
24.0%
3 440
22.4%
7 126
 
6.4%
Dash Punctuation
ValueCountFrequency (%)
- 2244
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8033
65.6%
Common 4204
34.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 1790
22.3%
V 1790
22.3%
G 1079
13.4%
P 872
10.9%
M 841
10.5%
A 841
10.5%
R 575
 
7.2%
Y 217
 
2.7%
N 14
 
0.2%
C 14
 
0.2%
Common
ValueCountFrequency (%)
- 2244
53.4%
1 924
22.0%
4 470
 
11.2%
3 440
 
10.5%
7 126
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12237
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2244
18.3%
T 1790
14.6%
V 1790
14.6%
G 1079
8.8%
1 924
7.6%
P 872
 
7.1%
M 841
 
6.9%
A 841
 
6.9%
R 575
 
4.7%
4 470
 
3.8%
Other values (5) 811
 
6.6%

runtime
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.643989
Minimum0
Maximum251
Zeros24
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:10.416734image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile21
Q144
median84
Q3105
95-th percentile140.75
Maximum251
Range251
Interquartile range (IQR)61

Descriptive statistics

Standard deviation39.47416
Coefficient of variation (CV)0.50839944
Kurtosis-0.41016214
Mean77.643989
Median Absolute Deviation (MAD)31
Skewness0.22024045
Sum450801
Variance1558.2093
MonotonicityNot monotonic
2023-11-27T22:42:10.686139image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
24 129
 
2.2%
90 122
 
2.1%
45 108
 
1.9%
95 105
 
1.8%
100 104
 
1.8%
44 102
 
1.8%
23 92
 
1.6%
25 85
 
1.5%
94 84
 
1.4%
105 83
 
1.4%
Other values (195) 4792
82.5%
ValueCountFrequency (%)
0 24
0.4%
2 4
 
0.1%
3 8
 
0.1%
4 5
 
0.1%
5 7
 
0.1%
6 11
0.2%
7 5
 
0.1%
8 8
 
0.1%
9 9
 
0.2%
10 12
0.2%
ValueCountFrequency (%)
251 1
< 0.1%
240 1
< 0.1%
235 1
< 0.1%
230 1
< 0.1%
229 1
< 0.1%
225 2
< 0.1%
224 1
< 0.1%
217 1
< 0.1%
213 1
< 0.1%
210 1
< 0.1%

genres
Text

Distinct1626
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:10.858224image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length96
Median length83
Mean length26.561488
Min length2

Characters and Unicode

Total characters154216
Distinct characters23
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1214 ?
Unique (%)20.9%

Sample

1st row['documentation']
2nd row['crime', 'drama']
3rd row['comedy', 'fantasy']
4th row['comedy']
5th row['horror']
ValueCountFrequency (%)
drama 2901
19.8%
comedy 2269
15.5%
thriller 1178
8.1%
action 1053
 
7.2%
romance 958
 
6.5%
documentation 910
 
6.2%
crime 891
 
6.1%
animation 665
 
4.5%
fantasy 631
 
4.3%
family 622
 
4.3%
Other values (10) 2548
17.4%
2023-11-27T22:42:11.306718image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 29116
18.9%
a 12769
 
8.3%
r 9521
 
6.2%
m 9454
 
6.1%
8820
 
5.7%
, 8820
 
5.7%
o 8384
 
5.4%
i 7852
 
5.1%
e 7437
 
4.8%
c 6906
 
4.5%
Other values (13) 45137
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 95848
62.2%
Other Punctuation 37936
 
24.6%
Space Separator 8820
 
5.7%
Open Punctuation 5806
 
3.8%
Close Punctuation 5806
 
3.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 12769
13.3%
r 9521
9.9%
m 9454
9.9%
o 8384
8.7%
i 7852
8.2%
e 7437
7.8%
c 6906
7.2%
n 6296
6.6%
d 6080
6.3%
t 6013
 
6.3%
Other values (8) 15136
15.8%
Other Punctuation
ValueCountFrequency (%)
' 29116
76.8%
, 8820
 
23.2%
Space Separator
ValueCountFrequency (%)
8820
100.0%
Open Punctuation
ValueCountFrequency (%)
[ 5806
100.0%
Close Punctuation
ValueCountFrequency (%)
] 5806
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 95848
62.2%
Common 58368
37.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 12769
13.3%
r 9521
9.9%
m 9454
9.9%
o 8384
8.7%
i 7852
8.2%
e 7437
7.8%
c 6906
7.2%
n 6296
6.6%
d 6080
6.3%
t 6013
 
6.3%
Other values (8) 15136
15.8%
Common
ValueCountFrequency (%)
' 29116
49.9%
8820
 
15.1%
, 8820
 
15.1%
[ 5806
 
9.9%
] 5806
 
9.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 154216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 29116
18.9%
a 12769
 
8.3%
r 9521
 
6.2%
m 9454
 
6.1%
8820
 
5.7%
, 8820
 
5.7%
o 8384
 
5.4%
i 7852
 
5.1%
e 7437
 
4.8%
c 6906
 
4.5%
Other values (13) 45137
29.3%

production_countries
Text

Distinct449
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:11.555534image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length42
Median length6
Mean length6.7917671
Min length2

Characters and Unicode

Total characters39433
Distinct characters36
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique312 ?
Unique (%)5.4%

Sample

1st row['US']
2nd row['US']
3rd row['GB']
4th row['GB']
5th row['US']
ValueCountFrequency (%)
us 2327
34.6%
in 629
 
9.4%
gb 406
 
6.0%
jp 291
 
4.3%
fr 248
 
3.7%
232
 
3.4%
kr 216
 
3.2%
ca 216
 
3.2%
es 212
 
3.2%
de 139
 
2.1%
Other values (98) 1810
26.9%
2023-11-27T22:42:12.048445image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
' 12988
32.9%
[ 5806
14.7%
] 5806
14.7%
S 2655
 
6.7%
U 2454
 
6.2%
, 920
 
2.3%
920
 
2.3%
N 888
 
2.3%
I 827
 
2.1%
R 744
 
1.9%
Other values (26) 5425
13.8%

Most occurring categories

ValueCountFrequency (%)
Other Punctuation 13908
35.3%
Uppercase Letter 12987
32.9%
Open Punctuation 5806
14.7%
Close Punctuation 5806
14.7%
Space Separator 920
 
2.3%
Lowercase Letter 6
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
S 2655
20.4%
U 2454
18.9%
N 888
 
6.8%
I 827
 
6.4%
R 744
 
5.7%
B 603
 
4.6%
G 578
 
4.5%
E 535
 
4.1%
A 488
 
3.8%
P 463
 
3.6%
Other values (16) 2752
21.2%
Lowercase Letter
ValueCountFrequency (%)
n 2
33.3%
e 1
16.7%
b 1
16.7%
a 1
16.7%
o 1
16.7%
Other Punctuation
ValueCountFrequency (%)
' 12988
93.4%
, 920
 
6.6%
Open Punctuation
ValueCountFrequency (%)
[ 5806
100.0%
Close Punctuation
ValueCountFrequency (%)
] 5806
100.0%
Space Separator
ValueCountFrequency (%)
920
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 26440
67.1%
Latin 12993
32.9%

Most frequent character per script

Latin
ValueCountFrequency (%)
S 2655
20.4%
U 2454
18.9%
N 888
 
6.8%
I 827
 
6.4%
R 744
 
5.7%
B 603
 
4.6%
G 578
 
4.4%
E 535
 
4.1%
A 488
 
3.8%
P 463
 
3.6%
Other values (21) 2758
21.2%
Common
ValueCountFrequency (%)
' 12988
49.1%
[ 5806
22.0%
] 5806
22.0%
, 920
 
3.5%
920
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39433
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
' 12988
32.9%
[ 5806
14.7%
] 5806
14.7%
S 2655
 
6.7%
U 2454
 
6.2%
, 920
 
2.3%
920
 
2.3%
N 888
 
2.3%
I 827
 
2.1%
R 744
 
1.9%
Other values (26) 5425
13.8%

seasons
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)1.1%
Missing3759
Missing (%)64.7%
Infinite0
Infinite (%)0.0%
Mean2.1656082
Minimum1
Maximum42
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:12.253522image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum42
Range41
Interquartile range (IQR)1

Descriptive statistics

Standard deviation2.6362073
Coefficient of variation (CV)1.2173057
Kurtosis74.502716
Mean2.1656082
Median Absolute Deviation (MAD)0
Skewness6.8673539
Sum4433
Variance6.9495889
MonotonicityNot monotonic
2023-11-27T22:42:12.498087image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1187
 
20.4%
2 374
 
6.4%
3 181
 
3.1%
4 116
 
2.0%
5 76
 
1.3%
6 40
 
0.7%
7 16
 
0.3%
8 14
 
0.2%
9 9
 
0.2%
11 7
 
0.1%
Other values (13) 27
 
0.5%
(Missing) 3759
64.7%
ValueCountFrequency (%)
1 1187
20.4%
2 374
 
6.4%
3 181
 
3.1%
4 116
 
2.0%
5 76
 
1.3%
6 40
 
0.7%
7 16
 
0.3%
8 14
 
0.2%
9 9
 
0.2%
10 5
 
0.1%
ValueCountFrequency (%)
42 1
 
< 0.1%
39 1
 
< 0.1%
37 1
 
< 0.1%
29 1
 
< 0.1%
24 3
0.1%
21 1
 
< 0.1%
19 1
 
< 0.1%
18 1
 
< 0.1%
15 4
0.1%
14 2
< 0.1%

imdb_id
Text

MISSING 

Distinct5362
Distinct (%)100.0%
Missing444
Missing (%)7.6%
Memory size45.5 KiB
2023-11-27T22:42:12.743238image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Length

Max length10
Median length9
Mean length9.3060425
Min length9

Characters and Unicode

Total characters49899
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5362 ?
Unique (%)100.0%

Sample

1st rowtt0075314
2nd rowtt0071853
3rd rowtt0079470
4th rowtt0070047
5th rowtt0063929
ValueCountFrequency (%)
tt9266592 1
 
< 0.1%
tt0068562 1
 
< 0.1%
tt0079470 1
 
< 0.1%
tt0070047 1
 
< 0.1%
tt0063929 1
 
< 0.1%
tt0066999 1
 
< 0.1%
tt0058385 1
 
< 0.1%
tt0080453 1
 
< 0.1%
tt0061418 1
 
< 0.1%
tt0060862 1
 
< 0.1%
Other values (5352) 5352
99.8%
2023-11-27T22:42:13.310928image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
t 10724
21.5%
1 5107
10.2%
0 4557
9.1%
8 4181
 
8.4%
4 4118
 
8.3%
6 4117
 
8.3%
2 4099
 
8.2%
3 3304
 
6.6%
7 3298
 
6.6%
5 3240
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 39175
78.5%
Lowercase Letter 10724
 
21.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 5107
13.0%
0 4557
11.6%
8 4181
10.7%
4 4118
10.5%
6 4117
10.5%
2 4099
10.5%
3 3304
8.4%
7 3298
8.4%
5 3240
8.3%
9 3154
8.1%
Lowercase Letter
ValueCountFrequency (%)
t 10724
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 39175
78.5%
Latin 10724
 
21.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 5107
13.0%
0 4557
11.6%
8 4181
10.7%
4 4118
10.5%
6 4117
10.5%
2 4099
10.5%
3 3304
8.4%
7 3298
8.4%
5 3240
8.3%
9 3154
8.1%
Latin
ValueCountFrequency (%)
t 10724
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 49899
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
t 10724
21.5%
1 5107
10.2%
0 4557
9.1%
8 4181
 
8.4%
4 4118
 
8.3%
6 4117
 
8.3%
2 4099
 
8.2%
3 3304
 
6.6%
7 3298
 
6.6%
5 3240
 
6.5%

imdb_score
Real number (ℝ)

MISSING 

Distinct81
Distinct (%)1.5%
Missing523
Missing (%)9.0%
Infinite0
Infinite (%)0.0%
Mean6.5334469
Minimum1.5
Maximum9.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:13.493028image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum1.5
5-th percentile4.5
Q15.8
median6.6
Q37.4
95-th percentile8.2
Maximum9.6
Range8.1
Interquartile range (IQR)1.6

Descriptive statistics

Standard deviation1.1609316
Coefficient of variation (CV)0.17769052
Kurtosis0.78618261
Mean6.5334469
Median Absolute Deviation (MAD)0.8
Skewness-0.65989633
Sum34516.2
Variance1.3477622
MonotonicityNot monotonic
2023-11-27T22:42:13.635397image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.6 201
 
3.5%
6.8 199
 
3.4%
6.5 193
 
3.3%
6.2 192
 
3.3%
7.4 190
 
3.3%
7.2 187
 
3.2%
7.3 183
 
3.2%
7.1 182
 
3.1%
6.7 181
 
3.1%
7 176
 
3.0%
Other values (71) 3399
58.5%
(Missing) 523
 
9.0%
ValueCountFrequency (%)
1.5 1
 
< 0.1%
1.6 1
 
< 0.1%
1.7 3
0.1%
1.8 1
 
< 0.1%
1.9 1
 
< 0.1%
2 1
 
< 0.1%
2.1 2
 
< 0.1%
2.2 2
 
< 0.1%
2.3 6
0.1%
2.4 1
 
< 0.1%
ValueCountFrequency (%)
9.6 2
 
< 0.1%
9.5 1
 
< 0.1%
9.3 3
 
0.1%
9.2 3
 
0.1%
9.1 2
 
< 0.1%
9 10
 
0.2%
8.9 6
 
0.1%
8.8 17
0.3%
8.7 24
0.4%
8.6 33
0.6%

imdb_votes
Real number (ℝ)

MISSING 

Distinct3831
Distinct (%)72.7%
Missing539
Missing (%)9.3%
Infinite0
Infinite (%)0.0%
Mean23407.195
Minimum5
Maximum2268288
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size45.5 KiB
2023-11-27T22:42:13.773607image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile52
Q1521
median2279
Q310144
95-th percentile115896.1
Maximum2268288
Range2268283
Interquartile range (IQR)9623

Descriptive statistics

Standard deviation87134.316
Coefficient of variation (CV)3.7225441
Kurtosis202.17161
Mean23407.195
Median Absolute Deviation (MAD)2107
Skewness11.305849
Sum1.232857 × 108
Variance7.592389 × 109
MonotonicityNot monotonic
2023-11-27T22:42:13.903560image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
43 11
 
0.2%
25 11
 
0.2%
172 9
 
0.2%
14 9
 
0.2%
30 9
 
0.2%
74 9
 
0.2%
38 9
 
0.2%
48 8
 
0.1%
6 8
 
0.1%
35 8
 
0.1%
Other values (3821) 5176
89.1%
(Missing) 539
 
9.3%
ValueCountFrequency (%)
5 4
0.1%
6 8
0.1%
7 6
0.1%
8 5
0.1%
9 6
0.1%
10 3
 
0.1%
11 6
0.1%
12 5
0.1%
13 5
0.1%
14 9
0.2%
ValueCountFrequency (%)
2268288 1
< 0.1%
1994599 1
< 0.1%
1727694 1
< 0.1%
1472668 1
< 0.1%
1346020 1
< 0.1%
989090 1
< 0.1%
945125 1
< 0.1%
795222 1
< 0.1%
748654 1
< 0.1%
723306 1
< 0.1%

Interactions

2023-11-27T22:42:05.159689image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:41:59.862712image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:00.931720image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:02.201492image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:03.144162image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:04.195550image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:05.292573image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:41:59.981430image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:01.152719image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:02.322970image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:03.349867image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:04.359649image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:05.414181image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:00.217858image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:01.340441image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:02.516751image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:03.554671image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:04.520100image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:05.552637image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:00.442097image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:01.484198image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:02.702482image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:03.712811image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:04.694118image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:05.699859image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:00.584253image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:01.689455image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:02.845351image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:03.854851image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:04.865276image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:05.827925image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:00.806320image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:02.007090image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:02.990464image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:04.049489image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
2023-11-27T22:42:05.017426image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/

Correlations

2023-11-27T22:42:14.133018image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
age_certificationdf_indeximdb_scoreimdb_votesrelease_yearruntimeseasonstype
age_certification1.0000.2170.242-0.3030.233-0.7280.0790.999
df_index0.2171.000-0.179-0.2780.962-0.120-0.5410.178
imdb_score0.242-0.1791.0000.219-0.137-0.1780.1500.331
imdb_votes-0.303-0.2780.2191.000-0.1590.2400.2860.038
release_year0.2330.962-0.137-0.1591.000-0.141-0.5120.119
runtime-0.728-0.120-0.1780.240-0.1411.000-0.2510.805
seasons0.079-0.5410.1500.286-0.512-0.2511.0001.000
type0.9990.1780.3310.0380.1190.8051.0001.000

Missing values

2023-11-27T22:42:06.041137image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
A simple visualization of nullity by column.
2023-11-27T22:42:06.317887image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-11-27T22:42:06.539675image/svg+xmlMatplotlib v3.7.3, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

df_indexidtitletyperelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votes
00ts300399Five Came Back: The Reference FilmsSHOW1945TV-MA48['documentation']['US']1.0NaNNaNNaN
11tm84618Taxi DriverMOVIE1976R113['crime', 'drama']['US']NaNtt00753148.3795222.0
22tm127384Monty Python and the Holy GrailMOVIE1975PG91['comedy', 'fantasy']['GB']NaNtt00718538.2530877.0
33tm70993Life of BrianMOVIE1979R94['comedy']['GB']NaNtt00794708.0392419.0
44tm190788The ExorcistMOVIE1973R133['horror']['US']NaNtt00700478.1391942.0
55ts22164Monty Python's Flying CircusSHOW1969TV-1430['comedy', 'european']['GB']4.0tt00639298.872895.0
66tm14873Dirty HarryMOVIE1971R102['thriller', 'crime', 'action']['US']NaNtt00669997.7153463.0
77tm185072My Fair LadyMOVIE1964G170['drama', 'music', 'romance', 'family']['US']NaNtt00583857.894121.0
88tm98978The Blue LagoonMOVIE1980R104['romance', 'drama']['US']NaNtt00804535.869053.0
99tm119281Bonnie and ClydeMOVIE1967R110['drama', 'crime', 'action']['US']NaNtt00614187.7111189.0
df_indexidtitletyperelease_yearage_certificationruntimegenresproduction_countriesseasonsimdb_idimdb_scoreimdb_votes
57965796ts286386The Big DaySHOW2021TV-MA45['reality', 'romance']['US']2.0tt138875184.6327.0
57975797tm985215Princess 'Daya'ReeseMOVIE2021NaN115['romance', 'comedy']['PH']NaNtt133998027.245.0
57985798tm1004011Time to DanceMOVIE2021NaN107['drama', 'romance']['IN']NaNtt86222322.2950.0
57995799ts307884HQ BarbersSHOW2021TV-1424['comedy']['NG']1.0NaNNaNNaN
58005800tm1040816Momshies! Your Soul is MineMOVIE2021NaN108['comedy']['PH']NaNtt144122405.826.0
58015801tm1014599Fine WineMOVIE2021NaN100['romance', 'drama']['NG']NaNtt138574806.939.0
58025802tm1108171Edis StarlightMOVIE2021NaN74['music', 'documentation'][]NaNNaNNaNNaN
58035803tm1045018ClashMOVIE2021NaN88['family', 'drama']['NG', 'CA']NaNtt146207326.532.0
58045804tm1098060Shadow PartiesMOVIE2021NaN116['action', 'thriller'][]NaNtt101680946.29.0
58055805ts271048Mighty Little Bheem: Kite FestivalSHOW2021NaN0['family', 'comedy', 'animation'][]1.0tt137110948.816.0